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Recognition of Depression in Bipolar Disorder: Leveraging Cohort and Person-Specific Knowledge

Soheil Khorram, John Gideon, Melvin McInnis, Emily Mower Provost

Individuals with bipolar disorder typically exhibit changes in theacoustics of their speech. Mobile health systems seek to model thesechanges to automatically detect and correctly identify current statesin an individual and to ultimately predict impending mood episodes.We have developed a program, PRIORI (Predicting Individual Outcomesfor Rapid Intervention), that analyzes acoustics of speech as predictorsof mood states from mobile smartphone data. Mood prediction systemsgenerally assume that the symptomatology of an individual can be modeledusing patterns common in a cohort population due to limitations inthe size of available datasets. However, individuals are unique. Thispaper explores person-level systems that can be developed from thecurrent PRIORI database of an extensive and longitudinal collectioncomposed of two subsets: a smaller labeled portion and a larger unlabeledportion. The person-level system employs the unlabeled portion to extracti-vectors, which characterize single individuals. The labeled portionis then used to train person-level and population-level supervisedclassifiers, operating on the i-vectors and on speech rhythm statistics,respectively. The unification of these two approaches results in asignificant improvement over the baseline system, demonstrating theimportance of a multi-level approach to capturing depression symptomatology.